Code
import eia_api as api
import eia_data
import pandas as pd
import numpy as np
import requests
import json
import os
import datetime
import plotly.express as pxEIA API - Data Backfill
The goal of this doc is to execute an initial data pull of the hourly demand for California balancing authority subregion (CISO). This includes the following four independent system operators:
- Pacific Gas and Electric (PGAE)
- Southern California Edison (SCE)
- San Diego Gas and Electric (SDGE)
- Valley Electric Association (VEA)
The data backfill process includes the following steps:
- Setting parameters and pulling the data
- Data quality checks
- Saving the data and creating a log file
Load Libraries
Code
raw_json = open("../metadata/series.json")
meta_json = json.load(raw_json)
series = pd.DataFrame(meta_json["series"])
api_path = meta_json["api_path"]Code
facets_template = {
"parent" : None,
"subba" : None
}
start = datetime.datetime(2018, 7, 1, 8)
end = datetime.datetime(2024, 2, 18, 1)
offset = 2250
eia_api_key = os.getenv('EIA_API_KEY')
meta_path = "../metadata/ciso_log_py.csv"
data_path = "../csv/ciso_grid_py.csv"Code
metadata = api.eia_metadata(api_key = eia_api_key, api_path = api_path)
print(metadata.meta.keys())
print(metadata.meta["startPeriod"])
print(metadata.meta["endPeriod"])dict_keys(['id', 'name', 'description', 'frequency', 'facets', 'data', 'startPeriod', 'endPeriod', 'defaultDateFormat', 'defaultFrequency'])
2018-06-19T05
2024-02-24T08Code
for i in series.index:
facets = facets_template
facets["parent"] = series.at[i, "parent_id"]
facets["subba"] = series.at[i, "subba_id"]
print(facets)
temp = api.eia_backfill(api_key = eia_api_key,
api_path = api_path+ "data",
facets = facets,
start = start,
end = end,
offset = offset)
ts_obj = pd.DataFrame(np.arange(start = start, stop = end + datetime.timedelta(hours = 1), step = datetime.timedelta(hours = 1)).astype(datetime.datetime), columns=["index"])
ts_obj = ts_obj.merge(temp.data, left_on = "index", right_on = "period", how="left")
ts_obj.drop("period", axis = 1, inplace= True)
ts_obj = ts_obj.rename(columns= {"index": "period"})
meta_temp = eia_data.create_metadata(data = ts_obj, start = start, end = end, type = "backfill")
meta_temp["index"] = 1
meta_df = pd.DataFrame([meta_temp])
if i == series.index.start:
data = ts_obj
meta = meta_df
else:
data = data._append(ts_obj)
meta = meta._append(meta_df){'parent': 'CISO', 'subba': 'PGAE'}
{'parent': 'CISO', 'subba': 'SCE'}
{'parent': 'CISO', 'subba': 'SDGE'}
{'parent': 'CISO', 'subba': 'VEA'}Code
print(meta)
# The initial pull has some missing values
data.head() index parent subba time start \
0 1 CISO PGAE 2024-02-24 19:41:53.318830+00:00 2018-07-01 08:00:00
0 1 CISO SCE 2024-02-24 19:42:08.307761+00:00 2018-07-01 08:00:00
0 1 CISO SDGE 2024-02-24 19:42:25.350882+00:00 2018-07-01 08:00:00
0 1 CISO VEA 2024-02-24 19:42:40.175097+00:00 2018-07-01 08:00:00
end start_act end_act start_match \
0 2024-02-18 01:00:00 2018-07-01 08:00:00 2024-02-18 01:00:00 True
0 2024-02-18 01:00:00 2018-07-01 08:00:00 2024-02-18 01:00:00 True
0 2024-02-18 01:00:00 2018-07-01 08:00:00 2024-02-18 01:00:00 True
0 2024-02-18 01:00:00 2018-07-01 08:00:00 2024-02-18 01:00:00 True
end_match n_obs na type update success \
0 True 49386 98 backfill False False
0 True 49386 98 backfill False False
0 True 49386 98 backfill False False
0 True 49386 98 backfill False False
comments
0 Missing values were found;
0 Missing values were found;
0 Missing values were found;
0 Missing values were found; | period | subba | subba-name | parent | parent-name | value | value-units | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-07-01 08:00:00 | PGAE | Pacific Gas and Electric | CISO | California Independent System Operator | 12522.0 | megawatthours |
| 1 | 2018-07-01 09:00:00 | PGAE | Pacific Gas and Electric | CISO | California Independent System Operator | 11745.0 | megawatthours |
| 2 | 2018-07-01 10:00:00 | PGAE | Pacific Gas and Electric | CISO | California Independent System Operator | 11200.0 | megawatthours |
| 3 | 2018-07-01 11:00:00 | PGAE | Pacific Gas and Electric | CISO | California Independent System Operator | 10822.0 | megawatthours |
| 4 | 2018-07-01 12:00:00 | PGAE | Pacific Gas and Electric | CISO | California Independent System Operator | 10644.0 | megawatthours |
Code
# Save the data
d = eia_data.append_data(data_path = data_path, new_data = data, init = True, save = True)
# Save the metadata
meta["success"] = True
meta["update"] = True
m = eia_data.append_metadata(meta_path = "../metadata/ciso_log_py.csv", meta = meta, save = True, init = True)
print(m)Initial data pull
Save the data to CSV file
index parent subba time start \
0 1 CISO PGAE 2024-02-24 19:41:53.318830+00:00 2018-07-01 08:00:00
0 1 CISO SCE 2024-02-24 19:42:08.307761+00:00 2018-07-01 08:00:00
0 1 CISO SDGE 2024-02-24 19:42:25.350882+00:00 2018-07-01 08:00:00
0 1 CISO VEA 2024-02-24 19:42:40.175097+00:00 2018-07-01 08:00:00
end start_act end_act start_match \
0 2024-02-18 01:00:00 2018-07-01 08:00:00 2024-02-18 01:00:00 True
0 2024-02-18 01:00:00 2018-07-01 08:00:00 2024-02-18 01:00:00 True
0 2024-02-18 01:00:00 2018-07-01 08:00:00 2024-02-18 01:00:00 True
0 2024-02-18 01:00:00 2018-07-01 08:00:00 2024-02-18 01:00:00 True
end_match n_obs na type update success \
0 True 49386 98 backfill True True
0 True 49386 98 backfill True True
0 True 49386 98 backfill True True
0 True 49386 98 backfill True True
comments
0 Missing values were found;
0 Missing values were found;
0 Missing values were found;
0 Missing values were found; Plot the Series
We will use Plotly to visualize the series:
Code
d = data.sort_values(by = ["subba", "period"])
p = px.line(d, x="period", y="value", color="subba")
p.show()